zhulf0804/ 3D-PointCloud

autonomous-driving

point-cloud

Python

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Papers and Datasets about Point Cloud.

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3D - Point Cloud

Name: 3D-PointCloud
Brand: 3D-PointCloud
SKU: //zhulf0804/3D-PointCloud
Rating: 2.93 (472 reviews)

Paper list and Datasets about Point Cloud. Datasets can be found in Datasets.md.

Survey papers

3D Object Detection for Autonomous Driving: A Survey [arXiv 2021]
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey [arXiv 2021]
3D Semantic Scene Completion: a Survey [arXiv 2021]
Deep Learning based 3D Segmentation: A Survey [arXiv 2021]
A comprehensive survey on point cloud registration [arXiv 2021]
Deep Learning for 3D Point Clouds: A Survey [TPAMI 2020]
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors [IJCV 2016]

2021

ICCV
- PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds [
```
registration
```
  ; PyTorch]
- An End-to-End Transformer Model for 3D Object Detection [
```
det
```
  ; PyTorch]
- Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration [
```
registration
```
  ; Github]
- Deep Hough Voting for Robust Global Registration [
```
registration
```
  ]
- Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection [
```
det
```
  ]
- Voxel Transformer for 3D Object Detection [
```
det
```
  ]
- Learning Inner-Group Relations on Point Clouds [
```
cls
```
  ,
```
seg
```
  ]
- Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds [
```
self-supervised
```
  ; Github]
- 4D-Net for Learned Multi-Modal Alignment [
```
det
```
  ]
- AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [
```
monocular
```
  ,
```
det
```
  ; Github]
- A Robust Loss for Point Cloud Registration [
```
registration
```
  ]
- OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration [
```
registration
```
  ]
- Improving 3D Object Detection with Channel-wise Transformer [
```
det
```
  ; Github]
- Voxel-based Network for Shape Completion by Leveraging Edge Generation [
```
completion
```
  ; Github]
- Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [
```
tracking
```
  ]
- ME-PCN: Point Completion Conditioned on Mask Emptiness [
```
completion
```
  ]
- Deep Hybrid Self-Prior for Full 3D Mesh Generation [
```
generation
```
  ]
- Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation [
```
monocular
```
  ,
```
depth
```
  ; Github]
- StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation [
```
monocular
```
  ,
```
depth
```
  ; PyTorch]
- Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility [
```
reconstruction
```
  ; Github]
- PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [
```
completion
```
  ; PyTorch]
- Adaptive Graph Convolution for Point Cloud Analysis [
```
cls
```
  ,
```
seg
```
  ; PyTorch]
- RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection [
```
det
```
  ]
- Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation [
```
monocular
```
  ,
```
depth
```
  ; Github]
- Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks [
```
seg
```
  ; Github]
- Is Pseudo-Lidar needed for Monocular 3D Object detection? [
```
monocular
```
  ,
```
det
```
  ; Github]
- Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
- Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis [
```
cls
```
  ,
```
seg
```
  ; Github]
- AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds [
```
normal estimation
```
  ; Github]
- Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather [
```
det
```
  ; Github]
- Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds [
```
tracking
```
  ; Github]
- SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [
```
completion
```
  ; Github]
- DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation [
```
seg
```
  ]
- RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection [
```
det
```
  ; Github]
- Hierarchical Aggregation for 3D Instance Segmentation [
```
seg
```
  ; Github]
- Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation [
```
seg
```
  ; Github]
- Group-Free 3D Object Detection via Transformers [
```
det
```
  ; PyTorch]
- VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation [
```
seg
```
  ; Github]
- Learning with Noisy Labels for Robust Point Cloud Segmentation [
```
seg
```
  ; Github]
- Geometry Uncertainty Projection Network for Monocular 3D Object Detection [
```
det
```
  ,
```
monocular
```
  ]
- ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation [
```
seg
```
  ]
- Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [
```
det
```
  ]
- Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion [
```
pre-training
```
  ; Github]
- HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration [
```
registration
```
  ; PyTorch]
- Score-Based Point Cloud Denoising [
```
denoising
```
  ]
- Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows [
```
monocular
```
  ,
```
pose
```
  ; Github]
- A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation [
```
seg
```
  ; Github]
CVPR
- To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels [
```
det
```
  ]
- RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection [
```
det
```
  ]
- Point Cloud Upsampling via Disentangled Refinement [
```
upsampling
```
  ; Github]
- Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning [
```
seg
```
  ]
- Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts [
```
seg
```
  ; PyTorch]
- PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks [
```
upsampling
```
  ; Tensorflow]
- Self-Point-Flow: Self-Supervised Scene Flow Estimation from Points Clouds with Optimal Transport and Random Walk [
```
scene flow
```
  ]
- SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data [
```
reconstruction
```
  ]
- HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding [
```
scene flow
```
  ]
- 3D Spatial Recognition without Spatially Labeled 3D [
```
det
```
  ,
```
seg
```
  ]
- LASR: Learning Articulated Shape Reconstruction from a Monocular Video [
```
reconstruction
```
  ,
```
monocular
```
  ]
- VoxelContext-Net: An Octree based Framework for Point Cloud Compression [
```
compression
```
  ]
- Unsupervised 3D Shape Completion through GAN Inversion [
```
completion
```
  ; PyTorch]
- KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control [Github]
- Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [
```
autonomous driving
```
  ; PyTorch]
- Self-Supervised Pillar Motion Learning for Autonomous Driving [
```
autonomous driving
```
  ; Github]
- Variational Relational Point Completion Network [
```
completion
```
  ]
- Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds [
```
det
```
  ; Github]
- RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation [
```
registration
```
  ]
- Objects are Different: Flexible Monocular 3D Object Detection [
```
det
```
  ; Github]
- FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds [
```
scene flow
```
  ]
- HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection [
```
det
```
  ]
- Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation [
```
seg
```
  ; PyTorch]
- ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning [
```
reg
```
  ; PyTorch]
- LiDAR R-CNN: An Efficient and Universal 3D Object Detector [
```
det
```
  ; Github]
- Equivariant Point Network for 3D Point Cloud Analysis [Github]
- PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds [
```
cls
```
  ,
```
det
```
  ; Github]
- Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection [
```
det
```
  ; Github]
- Delving into Localization Errors for Monocular 3D Object Detection [
```
det
```
  ; Github]
- M3DSSD: Monocular 3D Single Stage Object Detector [
```
det
```
  ; Github]
- Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding [
```
completion
```
  ]
- Monte Carlo Scene Search for 3D Scene Understanding
- Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion [
```
seg
```
  ; Github]
- PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency [
```
registration
```
  ; PyTorch]
- ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection [
```
det
```
  ; OpenPCDet]
- Robust Point Cloud Registration Framework Based on Deep Graph Matching [
```
registration
```
  ; Github]
- RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction [
```
reconstruction
```
  ]
- MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization [
```
motion analysis
```
  ; Github]
- TPCN: Temporal Point Cloud Networks for Motion Forecasting [
```
motion forecasting
```
  ]
- Self-supervised Geometric Perception [
```
self-supervised
```
  ; Github]
- PointGuard: Provably Robust 3D Point Cloud Classification [
```
cls
```
  ]
- Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos
- SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud [
```
det
```
  ; Github]
- Center-based 3D Object Detection and Tracking [
```
det
```
  ,
```
tracking
```
  ; PyTorch]
- 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection [
```
det
```
  ; PyTorch]
- Style-based Point Generator with Adversarial Rendering for Point Cloud Completion [
```
completion
```
  ]
- FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation [
```
pose estimation
```
  ; Github]
- Diffusion Probabilistic Models for 3D Point Cloud Generation [
```
generation
```
  ; Github]
- GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation [
```
pose estimation
```
  ; Github]
- PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers [
```
reconstruction
```
  ]
- PREDATOR: Registration of 3D Point Clouds with Low Overlap [
```
registration
```
  ; PyTorch]
- SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration [
```
registration
```
  ; Github]
- Categorical Depth Distribution Network for Monocular 3D Object Detection [
```
det
```
  ]
- Multimodal Motion Prediction with Stacked Transformers [
```
motion prediction
```
  ; Github]
- GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection [
```
det
```
  ; PyTorch]
- Model-based 3D Hand Reconstruction via Self-Supervised Learning [
```
reconstruction
```
  ]
- MonoRUn: Monocular 3D Object Detection by Self-Supervised Reconstruction and Uncertainty Propagation [
```
det
```
  ; Github]
- Deep Implicit Moving Least-Squares Functions for 3D Reconstruction [
```
reconstruction
```
  ; Tensorflow]
- Skeleton Merger: an Unsupervised Aligned Keypoint Detector [
```
keypoint
```
  ; PyTorch]
- Single Image Depth Prediction with Wavelet Decomposition [
```
depth
```
  ; PyTorch]
- 3D Shape Generation with Grid-based Implicit Functions [
```
generation
```
  ]
Others
- DeepBBS: Deep Best Buddies for Point Cloud Registration [
```
registration
```
  ; PyTorch; 3DV]
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [
```
seg
```
  ,
```
det
```
  ; PyTorch; TPAMI]
- Similarity-Aware Fusion Network for 3D Semantic Segmentation [
```
seg
```
  ; Github; IROS]
- Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection [
```
det
```
  ; ACM MM]
- From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder [
```
det
```
  ; Github; ACM MM]
- Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud [
```
det
```
  ; Github; ACM MM]
- Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views [
```
unsupervised
```
  ; ACM MM]
- SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [
```
upsampling
```
  ; Github; ACM MM]
- Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning [
```
self-supervised
```
  ; ACM MM]
- Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting [
```
monocular
```
  ,
```
det
```
  ; ACM MM]
- Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline [
```
cls
```
  ; PyTorch; ICML]
- GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors [
```
seg
```
  ; RAL]
- Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration [
```
registration
```
  ; IJCAI]
- PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery [
```
sampling
```
  ; Github; IJCAI]
- Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective [
```
completion
```
  ; TOG]
- Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [
```
seg
```
  ; TPAMI]
- Fast and Robust Iterative Closest Point [
```
registration
```
  ; Github; TPAMI]
- MonoGRNet: A General Framework for Monocular 3D Object Detection [
```
monocular
```
  ,
```
det
```
  ; TPAMI]
- Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network [
```
autonomous driving
```
  ; NeurIPS]
- PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds [
```
wireframe
```
  ; ICLR]
- Self-Guided Instance-Aware Network for Depth Completion and Enhancement [
```
depth
```
  ; ICRA]
- FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection [
```
det
```
  ; Github; ICRA]
- Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks [
```
cls
```
  ,
```
seg
```
  ; ICRA]
- 3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs [
```
keypoint
```
  ; Github; ICRA]
- NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation [
```
localisation
```
  ; ICRA]
- Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation [
```
depth estimation
```
  ; ICRA]
- YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection [
```
det
```
  ; PyTorch; ICRA]
- ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building [
```
static map
```
  ; ICRA]
- CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds [
```
pose estimation
```
  ; Tensorflow; ICRA]
- PointCutMix: Regularization Strategy for Point Cloud Classification [
```
cls
```
  ; code; ICML]
- Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud [
```
seg
```
  ; Github; AAAI]
- PointINet: Point Cloud Frame Interpolation Network [
```
frame interpolation
```
  ; PyTorch; AAAI]
- Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds [
```
seg
```
  ; code; AAAI]
- Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection [
```
det
```
  ; AAAI]
- Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [
```
cls
```
  ,
```
seg
```
  ; AAAI]
- CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud [
```
det
```
  ; PyTorch; AAAI]
- Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion [
```
seg
```
  ; Github; AAAI]
- labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds [
```
labeling tool
```
  ; CAD]
arXiv
- 3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation [
```
det
```
  ,
```
tracking
```
  ]
- PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds [PyTorch]
- Differentiable Convolution Search for Point Cloud Processing [
```
cls
```
  ,
```
seg
```
  ]
- SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering [
```
seg
```
  ]
- You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors [
```
registration
```
  ; PyTorch]
- GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network [
```
seg
```
  ]
- SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation [
```
det
```
  ]
- Progressive Coordinate Transforms for Monocular 3D Object Detection [
```
monocular
```
  ,
```
det
```
  ; Github]
- UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [
```
registration
```
  ]
- Investigating Attention Mechanism in 3D Point Cloud Object Detection [
```
det
```
  ; Github]
- Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness [
```
det
```
  ]
- Probabilistic and Geometric Depth: Detecting Objects in Perspective [
```
det
```
  ; Github]
- Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [
```
det
```
  ; Github]
- CarveNet: Carving Point-Block for Complex 3D Shape Completion [
```
completion
```
  ]
- CKConv: Learning Feature Voxelization for Point Cloud Analysis [
```
cls
```
  ,
```
seg
```
  ]
- DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization [
```
det
```
  ]
- Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters [
```
seg
```
  ]
- Dynamic Convolution for 3D Point Cloud Instance Segmentation[
```
seg
```
  ; PyTorch]
- Beyond Farthest Point Sampling in Point-Wise Analysis [
```
sampling
```
  ]
- Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes [
```
seg
```
  ]
- Multi-Modality Task Cascade for 3D Object Detection [
```
det
```
  ; Github]
- Point Cloud Registration using Representative Overlapping Points [
```
registration
```
  ; PyTorch]
- “Zero Shot” Point Cloud Upsampling [
```
upsampling
```
  ]
- Shape registration in the time of transformers [
```
registration
```
  ]
- 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching [
```
registration
```
  ]
- Image2Point: 3D Point-Cloud Understanding withPretrained 2D ConvNets [
```
cls
```
  ,
```
seg
```
  ; Github]
- FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration [
```
registration
```
  ]
- Z2P: Instant Rendering of Point Clouds [
```
rendering
```
  ]
- TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields [
```
place recognition
```
  ]
- Generalisable and distinctive 3D local deep descriptors for point cloud registration [
```
registration
```
  ]
- Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration [
```
registration
```
  ]
- M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers [
```
det
```
  ]
- FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [
```
monocular
```
  ,
```
det
```
  ; mmdet3D]
- Boundary-Aware 3D Object Detection from Point Clouds [
```
det
```
  ]
- Lidar Point Cloud Guided Monocular 3D Object Detection [
```
det
```
  ]
- Geometry-aware data augmentation for monocular 3D object detection [
```
det
```
  ]
- OCM3D: Object-Centric Monocular 3D Object Detection [
```
det
```
  ]
- Towards Efficient Graph Convolutional Networks for Point Cloud Handling [
```
network
```
  ; Github]
- Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds [
```
scene flow
```
  ]
- A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds [
```
UDA
```
  ]
- SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels [
```
seg
```
  ; Github]
- View-Guided Point Cloud Completion [
```
completion
```
  ]
- One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation [
```
seg
```
  ]
- Potential Convolution: Embedding Point Clouds into Potential Fields [
```
cls
```
  ,
```
seg
```
  ]
- 3D-MAN: 3D Multi-frame Attention Network for Object Detection [
```
det
```
  ]
- SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud [
```
det
```
  ; Github]
- 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning [
```
registration
```
  ; Github]
- Multi-view 3D Reconstruction with Transformer [
```
reconstruction
```
  ]
- X-view: Non-egocentric Multi-View 3D Object Detector [
```
det
```
  ]
- RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation [
```
seg
```
  ]
- 3DMNDT: 3D multi-view registration method based on the normal distributions transform [
```
registration
```
  ]
- SparsePoint: Fully End-to-End Sparse 3D Object Detector [
```
det
```
  ]
- S3Net: 3D LiDAR Sparse Semantic Segmentation Network [
```
seg
```
  ]
- Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions [
```
seg
```
  ]
- R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method [
```
registration
```
  ; Github]
- Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences [
```
autonomous driving
```
  ; Github]
- MapFusion: A General Framework for 3D Object Detection with HDMaps [
```
det
```
  ]
- Offboard 3D Object Detection from Point Cloud Sequences [
```
det
```
  ]
- A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding [
```
det
```
  ; PyTorch]
- IRON: Invariant-based Highly Robust Point Cloud Registration [
```
registration
```
  ]
- EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation [
```
cls
```
  ,
```
seg
```
  ]
- Pseudo-labeling for Scalable 3D Object Detection [
```
det
```
  ]
- LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment [
```
seg
```
  ]
- Scalable Scene Flow from Point Clouds in the Real World [
```
scene flow
```
  ]
- InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring [
```
visual grounding
```
  ]
- FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation [
```
seg
```
  ]
- P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching [
```
matching
```
  ]
- UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering [
```
registration
```
  ; PyTorch]
- Attention Models for Point Clouds in Deep Learning: A Survey [
```
attention
```
  ]
- EfficientLPS: Efficient LiDAR Panoptic Segmentation [
```
seg
```
  ]
- HyperPocket: Generative Point Cloud Completion [
```
completion
```
  ]
- Point-set Distances for Learning Representations of 3D Point Clouds [
```
representation
```
  ]
- DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds [
```
det
```
  ]
- PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [
```
det
```
  ; OpenPCDet]
- Self-Attention Based Context-Aware 3D Object Detection [
```
det
```
  ; PyTorch]
- A two-stage data association approach for 3D Multi-object Tracking [
```
tracking
```
  ]
- The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions [
```
seg
```
  ]
- Joint Learning of 3D Shape Retrieval and Deformation
- Self-Supervised Pretraining of 3D Features on any Point-Cloud [
```
det
```
  ,
```
seg
```
  ,
```
cls
```
  ; PyTorch]
- Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition [
```
place recognition
```
  ; Tensorflow]

2020

ECCV
- PointMixup: Augmentation for point cloud [
```
augmentation
```
  ,
```
cls
```
  ; PyTorch]
- Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations [
```
det
```
  ; PyTorch]
- Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets [
```
keypoints
```
  ]
- Weakly-supervised 3D Shape Completion in the Wild [
```
completion
```
  ]
- SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification [
```
completion
```
  ,
```
cls
```
  ; Github]
- Detail Preserved Point Cloud Completion via Separated Feature Aggregation [
```
completion
```
  ; Tensorflow]
- PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds [
```
flow estimation
```
  ; PyTorch]
- JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds [
```
seg
```
  ; Tensorflow]
- A Closer Look at Local Aggregation Operators in Point Cloud Analysis [
```
cls
```
  ,
```
seg
```
  ; Code]
- Instance-Aware Embedding for Point Cloud Instance Segmentation [
```
seg
```
  ]
- Multimodal Shape Completion via Conditional Generative Adversarial Networks [
```
completion
```
  ; PyTorch]
- GRNet: Gridding Residual Network for Dense Point Cloud Completion [
```
completion
```
  ; PyTorch]
- 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection [
```
det
```
  ]
- SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds [
```
det
```
  ; Github]
- Pillar-based Object Detection for Autonomous Driving [
```
det
```
  ,
```
autonomous driving
```
  ; Tensorflow]
- EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection [
```
det
```
  ; PyTorch]
- Finding Your (3D) Center: 3D Object Detection Using a Learned Loss [
```
det
```
  ; Tensorflow]
- Weakly Supervised 3D Object Detection from Lidar Point Cloud [
```
det
```
  ; PyTorch]
- H3DNet: 3D Object Detection Using Hybrid Geometric Primitives [
```
det
```
  ; Tensorflow]
- Generative Sparse Detection Networks for 3D Single-shot Object Detection [
```
det
```
  ; Github]
- Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution [
```
seg
```
  ,
```
det
```
  ; PyTorch]
- DeepGMR: Learning Latent Gaussian Mixture Models for Registration [
```
registration
```
  ; PyTorch]
- Quaternion Equivariant Capsule Networks for 3D Point Clouds [PyTorch]
- PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding [
```
unsupervised
```
  ;
```
cls
```
  ,
```
seg
```
  ,
```
det
```
  ; PyTorch]
- Convolutional Occupancy Networks [
```
reconstruction
```
  ; PyTorch]
- Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration [
```
registration
```
  ; PyTorch]
- Progressive Point Cloud Deconvolution Generation Network [
```
generation
```
  ; github]
- Reinforced Axial Refinement Network for Monocular 3D Object Detection [
```
det
```
  ,
```
monocular
```
  ]
- Monocular 3D Object Detection via Feature Domain Adaptation [
```
det
```
  ,
```
monocular
```
  ]
- Improving 3D Object Detection through Progressive Population Based Augmentation [
```
det
```
  ]
- An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds [
```
det
```
  ]
- Rotation-robust Intersection over Union for 3D Object Detection
- DPDist: Comparing Point Clouds Using Deep Point Cloud Distance
CVPR
- PointPainting: Sequential Fusion for 3D Object Detection [
```
det
```
  ]
- 3DSSD: Point-based 3D Single Stage Object Detector [
```
det
```
  ; Tensorflow]
- A Hierarchical Graph Network for 3D Object Detection on Point Clouds [
```
det
```
  ]
- Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence [
```
correspondences
```
  ; Tensorflow]
- Deep Global Registration [
```
registration
```
  ; PyTorch]
- 3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation [
```
seg
```
  ; Github]
- PointGMM: a Neural GMM Network for Point Clouds [
```
generation
```
  ,
```
registration
```
  ; PyTorch]
- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [
```
det
```
  ; Tensorflow]
- ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes [
```
det
```
  ]
- OccuSeg: Occupancy-aware 3D Instance Segmentation [
```
seg
```
  ]
- Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation [
```
seg
```
  ; PyTorch]
- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection [
```
det
```
  ; PyTorch]
- Going Deeper with Lean Point Networks [
```
seg
```
  ; PyTorch]
- Point Cloud Completion by Skip-attention Network with Hierarchical Folding [
```
completion
```
  ]
- Unsupervised Learning of Intrinsic Structural Representation Points [PyTorch]
- PF-Net: Point Fractal Network for 3D Point Cloud Completion [
```
completion
```
  ; PyTorch]
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [
```
det
```
  ; code]
- Adaptive Hierarchical Down-Sampling for Point Cloud Classification [
```
downsampling
```
  ,
```
cls
```
  ]
- SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud [
```
det
```
  ; PyTorch]
- 3DRegNet: A Deep Neural Network for 3D Point Registration [
```
registration
```
  ; Tensorflow]
- MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment [
```
non-rigid alignment
```
  ]
- SampleNet: Differentiable Point Cloud Sampling [
```
sample
```
  ,
```
cls
```
  ,
```
registration
```
  ,
```
reconstruction
```
  ; PyTorch]
- Learning multiview 3D point cloud registration [
```
multiview registration
```
  ; PyTorch]
- Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences [
```
registration
```
  ; PyTorch]
- PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [
```
unsupervised
```
  ;
```
cls
```
  ; PyTorch]
- Grid-GCN for Fast and Scalable Point Cloud Learning [
```
cls
```
  ,
```
seg
```
  ; mxnet]
- FPConv: Learning Local Flattening for Point Convolution [
```
cls
```
  ,
```
seg
```
  ; PyTorch]
- PointAugment: an Auto-Augmentation Framework for Point Cloud Classification [
```
cls
```
  ,
```
retrieval
```
  ; github]
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds [
```
seg
```
  ; Tensorflow]
- Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [
```
weakly supervised
```
  ;
```
seg
```
  ; Tensorflow]
- PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation [
```
seg
```
  ; PyTorch]
- Learning to Segment 3D Point Clouds in 2D Image Space [
```
seg
```
  ; Keras]
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation [
```
seg
```
  ; PyTorch]
- D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [
```
keypoints
```
  ,
```
registration
```
  ; Tensorflow, PyTorch]
- RPM-Net: Robust Point Matching using Learned Features [
```
registration
```
  ; PyTorch]
- Cascaded Refinement Network for Point Cloud Completion [
```
completion
```
  ; Tensorflow]
- P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds [
```
tracking
```
  ; PyTorch]
- An Efficient PointLSTM for Point Clouds Based Gesture Recognition [
```
gesture
```
  ; PyTorch]
Others
- Group Contextual Encoding for 3D Point Clouds [
```
det
```
  ,
```
cls
```
  ; PyTorch; NeurIPS]
- CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations [
```
dynamic sequences
```
  ; Github; NeurIPS]
- Skeleton-bridged Point Completion: From Global Inference to Local Adjustment [
```
completion
```
  ; NeurIPS]
- Self-Supervised Few-Shot Learning on Point Clouds [
```
cls
```
  ,
```
seg
```
  ; NeurIPS]
- Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud [
```
cls
```
  ; NeurIPS]
- PIE-NET: Parametric Inference of Point Cloud Edges [
```
edge det
```
  ; NeurIPS]
- Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds [
```
cls
```
  ,
```
seg
```
  ; Tensorflow; TPAMI]
- From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network [
```
det
```
  ; PyTorch; TPAMI]
- Unpaired Point Cloud Completion on Real Scans using Adversarial Training [
```
completion
```
  ; Tensorflow; ICLR]
- AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing [
```
cls
```
  ,
```
seg
```
  ; PyTorch; ICLR]
- Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds [ICLR]
- MSN: Morphing and Sampling Network for Dense Point Cloud Completion [
```
completion
```
  ; PyTorch; AAAI]
- TANet: Robust 3D Object Detection from Point Clouds with Triple Attention [
```
det
```
  ; PyTorch; AAAI]
- JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds [
```
seg
```
  ; Tensorflow]
- Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling [
```
cls
```
  ,
```
seg
```
  ; AAAI]
- Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution [
```
cls
```
  ,
```
seg
```
  ,
```
matching
```
  ; AAAI]
- Differentiable Manifold Reconstruction for Point Cloud Denoising [
```
denoising
```
  ; PyTorch; ACM MM]
- Weakly Supervised 3D Object Detection from Point Clouds [
```
det
```
  ; Tensorflow; ACM MM]
- Unsupervised Detection of Distinctive Regions on 3D Shapes [
```
unsupervised
```
  ; Tensorflow; TOG]
- Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds [
```
seg
```
  ,
```
cls
```
  ; Project; ICRA]
- Semantic Graph Based Place Recognition for 3D Point Clouds [
```
place recognition
```
  ; PyTorch; IROS]
- End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences [
```
registration
```
  ; PyTorch; IROS]
- Correspondence Matrices are Underrated [
```
registration, correspondence
```
  ; PyTorch; 3DV]
- Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks [
```
cls
```
  ,
```
seg
```
  ; 3DV]
- PanoNet3D: Combining Semantic and Geometric Understanding for LiDAR Point Cloud Detection [
```
det
```
  ; 3DV]
- FKAConv: Feature-Kernel Alignment for Point Cloud Convolution [
```
conv
```
  ,
```
cls
```
  ,
```
seg
```
  ; PyTorch; ACCV]
- Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks [
```
conv
```
  ,
```
cls
```
  ; ACCV]
- Reconstructing Human Body Mesh from Point Clouds by Adversarial GP Network [
```
reconstruction
```
  ; ACCV]
- Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds [
```
seg
```
  ; Tensorflow; ACCV]
- SDP-Net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3D Point Clouds [
```
det
```
  ; ACCV]
- Best Buddies Registration for Point Clouds [
```
registration
```
  ; PyTorch; ACCV]
- HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for Point Clouds Processing [
```
conv
```
  ,
```
cls
```
  ,
```
seg
```
  ; ACCV]
- SAUM: Symmetry-Aware Upsampling Module for Consistent Point Cloud Completion [
```
completion
```
  ; Tensorflow; ACCV]
- Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features [
```
registration
```
  ; Remote Sensing]
- ConvPoint: Continuous Convolutions for Point Cloud Processing [
```
cls
```
  ,
```
seg
```
  ; PyTorch; Computers & Graphics]
arXiv
- Multi-Modality Cut and Paste for 3D Object Detection [
```
det
```
  ; PyTorch]
- Self-Supervised Learning for Domain Adaptation on Point Clouds [
```
cls
```
  ,
```
seg
```
  ]
- SALA: Soft Assignment Local Aggregation for 3D Semantic Segmentation [
```
seg
```
  ]
- CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds [
```
correspondence
```
  ]
- Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation [
```
seg
```
  ]
- Geometric robust descriptor for 3D point cloud [
```
registration
```
  ,
```
cls
```
  ,
```
seg
```
  ]
- PCT: Point Cloud Transformer [
```
cls
```
  ,
```
seg
```
  ,
```
normal estimation
```
  ; Jittor]
- Point Transformer(Hengshuang Zhao) [
```
seg
```
  ,
```
cls
```
  ; PyTorch-unofficial]
- Point Transformer(Nico) [
```
cls
```
  ,
```
seg
```
  ]
- Deterministic PointNetLK for Generalized Registration [
```
registration
```
  ]
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation [
```
seg
```
  ; PyTorch]
- OcCo: Pre-Training by Completing Point Clouds [
```
pre-training
```
  ,
```
completion
```
  ; Github]
- Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration [
```
registration
```
  ]
- PRE-TRAINING BY COMPLETING POINT CLOUDS [
```
pre-training
```
  ,
```
cls
```
  ,
```
seg
```
  ; Github]
- Continuous Geodesic Convolutions for Learning on 3D Shapes [
```
descriptor
```
  ,
```
match
```
  ,
```
seg
```
  ]
- Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation [
```
seg
```
  ]
- A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds [
```
det
```
  ]
- TEASER: Fast and Certifiable Point Cloud Registration [
```
registration
```
  ; Github]
- Part-Aware Data Augmentation for 3D Object Detection in Point Cloud [
```
det
```
  ,
```
augmentation
```
  ; PyTorch]

2019

ICCV
- Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning [
```
denoising
```
  ; Tensorflow]
- 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions [
```
generation
```
  ; PyTorch]
- STD: Sparse-to-Dense 3D Object Detector for Point Cloud [
```
det
```
  ]
- USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds [
```
keypoints
```
  ,
```
registration
```
  ; PyTorch]
- LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis [
```
place recognition
```
  ]
- Unsupervised Multi-Task Feature Learning on Point Clouds [
```
cls
```
  ,
```
seg
```
  ]
- Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction [
```
unsupervised
```
  ,
```
cls
```
  ,
```
generation
```
  ,
```
seg
```
  ,
```
completion
```
  ]
- SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences [
```
dataset
```
  ]
- MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences [
```
cls
```
  ,
```
seg
```
  ,
```
flow estimation
```
  ; Tensorflow]
- DeepGCNs: Can GCNs Go as Deep as CNNs? [
```
seg
```
  ; Tensorflow]
- VV-NET: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation [
```
seg
```
  ; Github]
- Interpolated Convolutional Networks for 3D Point Cloud Understanding [
```
cls
```
  ,
```
seg
```
  ]
- Dynamic Points Agglomeration for Hierarchical Point Sets Learning [
```
cls
```
  ,
```
seg
```
  ]
- ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- Fast Point R-CNN [
```
det
```
  ]
- Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [
```
dataset
```
  ;
```
cls
```
  ; Tensorflow]
- KPConv: Flexible and Deformable Convolution for Point Clouds [
```
cls
```
  ,
```
seg
```
  ; code]
- Fully Convolutional Geometric Features [
```
match
```
  ; PyTorch]
- Deep Closest Point: Learning Representations for Point Cloud Registration [
```
registration
```
  ; PyTorch]
- DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration [
```
registration
```
  ]
- Efficient and Robust Registration on the 3D Special Euclidean Group [
```
registration
```
  ]
- Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation [
```
seg
```
  ]
- DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing [
```
cls
```
  ,
```
retrieval
```
  ,
```
seg
```
  ,
```
normal estimation
```
  ; PyTorch]
- DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense [
```
cls
```
  ]
- Efficient Learning on Point Clouds with Basis Point Sets [
```
cls
```
  ,
```
registration
```
  ; PyTorch]
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [
```
generation
```
  ,
```
reconstruction
```
  ; Pytorch
- PU-GAN: a Point Cloud Upsampling Adversarial Network [
```
upsampling
```
  ,
```
reconstruction
```
  ; Project]
- 3D Point Cloud Learning for Large-scale Environment Analysis and Place Recognition [
```
retrieval
```
  ,
```
place recognition
```
  ]
- Deep Hough Voting for 3D Object Detection in Point Clouds [
```
det
```
  ; PyTorch]
- Exploring the Limitations of Behavior Cloning for Autonomous Driving [
```
autonomous driving
```
  ; Pytorch]
CVPR
- LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [
```
det
```
  ; Github]
- TopNet: Structural Point Cloud Decoder [
```
completion
```
  ; Github]
- FlowNet3D: Learning Scene Flow in 3D Point Clouds [
```
scene flow
```
  ; Tensorflow]
- Occupancy Networks: Learning 3D Reconstruction in Function Space [
```
reconstruction
```
  ]
- Associatively Segmenting Instances and Semantics in Point Clouds [
```
seg
```
  ; Tensorflow]
- 3D Point Capsule Networks [
```
autoencoder
```
  ; PyTorch]
- Patch-based Progressive 3D Point Set Upsampling [
```
upsampling
```
  ; Tensorflow, PyTorch]
- Generating 3D Adversarial Point Clouds [
```
adversary
```
  ; Tensorflow]
- RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion [
```
completion
```
  ; PyTorch]
- GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud [
```
seg
```
  ; Tensorflow]
- JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields [
```
seg
```
  ; PyTorch]
- 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans [
```
seg
```
  ; PyTorch]
- Learning Transformation Synchronization [
```
transformation  synchronization
```
  ,
```
registration
```
  ; PyTorch]
- SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences [
```
registration
```
  ; Github]
- Learning Transformation Synchronization [
```
reconstruction
```
  ; PyTorch]
- 3D Local Features for Direct Pairwise Registration [
```
registration
```
  ]
- DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds [
```
registration
```
  ; Github]
- Relation-Shape Convolutional Neural Network for Point Cloud Analysis [
```
cls
```
  ,
```
seg
```
  ,
```
normal estimation
```
  ; PyTorch]
- Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN [
```
cls
```
  ,
```
det
```
  ; Tensorflow]
- 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks [
```
seg
```
  ; PyTorch]
- PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval [
```
retrieval
```
  ; Tensorflow]
- Attentional PointNet for 3D-Object Detection in Point Clouds [
```
det
```
  ; PyTorch]
- Octree guided CNN with Spherical Kernels for 3D Point Clouds [
```
cls
```
  ,
```
seg
```
  ; Github]
- A-CNN: Annularly Convolutional Neural Networks on Point Clouds [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- ClusterNet: Deep Hierarchical Cluster Network with Rigorously Rotation-Invariant Representation for Point Cloud Analysis [
```
cls
```
  ]
- Graph Attention Convolution for Point Cloud Semantic Segmentation [
```
seg
```
  ; PyTorch-unofficial]
- PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing [
```
seg
```
  ,
```
cls
```
  ; PyTorch]
- Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling [
```
cls
```
  ,
```
seg
```
  ,
```
gesture
```
  ]
- Learning to Sample [
```
sample
```
  ,
```
cls
```
  ,
```
retrieval
```
  ,
```
reconstruction
```
  ; Tensorflow]
- PointConv: Deep Convolutional Networks on 3D Point Clouds [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- The Perfect Match: 3D Point Cloud Matching With Smoothed Densities [
```
match
```
  ; code]
- PointNetLK: Point Cloud Registration using PointNet [
```
registration
```
  ; PyTorch]
- PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud [
```
det
```
  ; PyTorch]
- PointPillars: Fast Encoders for Object Detection From Point Clouds [
```
det
```
  ; Pytorch]
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [
```
depth estimation
```
  ,
```
det
```
  ; github]
- ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving [
```
dataset
```
  ,
```
autonomous driving
```
  ]
- Stereo R-CNN based 3D Object Detection for Autonomous Driving [
```
det
```
  ,
```
autonomous driving
```
  ; github]
- Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [
```
det
```
  ,
```
autonomous driving
```
  ; Tesorflow]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [
```
det
```
  ]
- GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving [
```
det
```
  ,
```
autonomous driving
```
  ]
- L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving [
```
autonomous driving
```
  ]
- Iterative Transformer Network for 3D Point Cloud [
```
pose
```
  ,
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
Others
- PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation [
```
domain adaptation
```
  ; PyTorch; NeurIPS]
- Learning elementary structures for 3D shape generation and matching [
```
generation
```
  ,
```
matching
```
  ; NeurIPS]
- Self-Supervised Deep Learning on Point Clouds by Reconstructing Space [
```
self-supervised, cls, seg
```
  ; NeurIPS]
- Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds [
```
seg
```
  ; Tensorflow; NeurIPS]
- PRNet: Self-Supervised Learning for Partial-to-Partial Registration [
```
registration
```
  ,
```
cls
```
  ; PyTorch; NeurIPS]
- Point-Voxel CNN for Efficient 3D Deep Learning [
```
seg
```
  ,
```
det
```
  ; PyTorch; NeurIPS]
- L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention [
```
autoencoder
```
  ; ACM MM]
- Deep Cascade Generation on Point Sets [
```
generation
```
  ; PyTorch; IJCAI]
- A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates [
```
registration
```
  ; RSS]
- Dynamic Graph CNN for Learning on Point Clouds [
```
cls
```
  ,
```
seg
```
  ; Github; TOG]
- SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud [
```
seg
```
  ; Tensorflow; ICRA]
- RangeNet++: Fast and Accurate LiDAR Semantic Segmentation [
```
seg
```
  ; PyTorch; IROS]
- AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects [
```
registration
```
  ; Tensorflow; 3DV]
- Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network [
```
reconstruction
```
  ; WACV]
arXiv
- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [
```
det
```
  ]
- PCRNet: Point Cloud Registration Network using PointNet Encoding [
```
registration
```
  ; PyTorch, Tensorflow]
- LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving [
```
autonomous driving
```
  ]
- Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]

2018

CVPR
- Learning 3D Shape Completion From Laser Scan Data With Weak Supervision [
```
completion
```
  ; Github]
- Deep Parametric Continuous Convolutional Neural Networks [
```
seg
```
  ,
```
motion estimation(lidar flow)
```
  ]
- Attentional ShapeContextNet for Point Cloud Recognition [
```
cls
```
  ,
```
seg
```
  ]
- A Papier-Mâché Approach to Learning 3D Surface Generation [
```
generation
```
  ; PyTorch]
- Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs [
```
seg
```
  ; PyTorch]
- FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation [
```
autoencoder
```
  ,
```
unsupervised
```
  ; code]
- FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis [
```
correspondence
```
  ,
```
seg
```
  ; Tensorflow]
- PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition [
```
retrieval
```
  ,
```
place recognition
```
  ; Tensorflow]
- PU-Net: Point Cloud Upsampling Network [
```
upsampling
```
  ; Tensorflow]
- SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation [
```
seg
```
  ; Tensorflow]
- Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling [
```
cls
```
  ,
```
seg
```
  ; code]
- Tangent Convolutions for Dense Prediction in 3D [
```
seg
```
  ; Tensorflow]
- PointGrid: A Deep Network for 3D Shape Understanding [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks [
```
seg
```
  ; Github]
- Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs [
```
seg
```
  ; PyTorch]
- SPLATNet: Sparse Lattice Networks for Point Cloud Processing [
```
seg
```
  ; Caffe]
- Pointwise Convolutional Neural Networks [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- SO-Net: Self-Organizing Network for Point Cloud Analysis [
```
autoencoder
```
  ,
```
cls
```
  ,
```
seg
```
  ; PyTorch]
- Recurrent Slice Networks for 3D Segmentation of Point Clouds [
```
seg
```
  ; PyTorch]
- PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [
```
registration
```
  ]
- PIXOR: Real-Time 3D Object Detection From Point Clouds [
```
det
```
  ; PyTorch]
- Frustum PointNets for 3D Object Detection From RGB-D Data [
```
det
```
  ; Tensorflow]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [
```
det
```
  ]
- 3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare [
```
reconstruction
```
  ]
- Multi-Level Fusion Based 3D Object Detection From Monocular Images [
```
det
```
  ]
ECCV
- 3D-CODED : 3D Correspondences by Deep Deformation [
```
matching
```
  ; PyTorch]
- SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues [
```
seg
```
  ,
```
cls
```
  ]
- Multiresolution Tree Networks for 3D Point Cloud Processing [
```
cls
```
  ,
```
generation
```
  ; PyTorch]
- HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration [
```
registration
```
  ; unofficial code]
- EC-Net: an Edge-aware Point set Consolidation Network [
```
consolidation
```
  ; Tensorflow]
- Learning and Matching Multi-View Descriptors for Registration of Point Clouds [
```
registration
```
  ]
- Local Spectral Graph Convolution for Point Set Feature Learning [
```
cls
```
  ,
```
seg
```
  ]
- 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation [
```
seg
```
  ]
- Fully-Convolutional Point Networks for Large-Scale Point Clouds [
```
seg
```
  ,
```
captioning
```
  ; Tensorflow]
- PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors [
```
registration
```
  ; PyTorch-unofficial]
- Deep Continuous Fusion for Multi-Sensor 3D Object Detection [
```
det
```
  ]
- 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [
```
match
```
  ,
```
registration
```
  ; Tensorflow]
- Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving [
```
autonomous driving
```
  ]
Others
- PointCNN: Convolution On X -Transformed Points [
```
cls
```
  ,
```
seg
```
  ; Tensorflow; NeurIPS]
- Learning Representations and Generative Models for 3D Point Clouds [
```
autoencoder
```
  ; Tensorflow; ICML]
- RGCNN: Regularized Graph CNN for Point Cloud Segmentation [
```
seg
```
  ,
```
cls
```
  ; Tensorflow; ACM MM]
- PCN: Point Completion Network [
```
completion
```
  ; Tensorflow; 3DV]
- Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration [
```
registration
```
  ; 3DV]
- Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods [
```
seg
```
  ; 3DV]
- Guaranteed Outlier Removal for Point Cloud Registration with Correspondences [
```
registration
```
  ; TPAMI]
- Second: Sparsely embedded convolutional detection [
```
det
```
  ;
```
Sensors
```
  ]
- Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving [
```
det
```
  ,
```
autonomous driving
```
  ; IEEE Robotics and Automation Letters]
- HDNET: Exploiting HD Maps for 3D Object Detection [
```
det
```
  ,
```
autonomous driving
```
  ; CoRL]
- Joint 3D Proposal Generation and Object Detection from View Aggregation [
```
det
```
  ,
```
autonomous driving
```
  ; IROS]
- Flex-Convolution(Million-Scale Point-Cloud Learning Beyond Grid-Worlds) [
```
cls
```
  ,
```
seg
```
  ; Tensorflow; ACCV]
- SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [
```
seg
```
  ; Tensorflow; ICRA]
- Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds [
```
seg
```
  ,
```
cls
```
  ,
```
normal estimation
```
  ; Tensorflow; TOG]
- Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction [
```
reconstruction
```
  ; Tensorflow; AAAI]
arXiv
- Spherical Convolutional Neural Network for 3D Point Clouds [
```
cls
```
  ]
- Point Convolutional Neural Networks by Extension Operators [
```
cls
```
  ,
```
seg
```
  ,
```
normal estimation
```
  ; Tensorflow]
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation [
```
seg
```
  ; Tensorflow]
- Point Cloud GAN [
```
generation
```
  ; PyTorch]
- Roarnet: A robust 3d object detection based on region approximation refinement [
```
det
```
  ]
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds [
```
det
```
  ; PyTorch]
- Classification of Point Cloud Scenes with Multiscale Voxel Deep Network [
```
seg
```
  ]

2017

CVPR
- Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis [
```
completion
```
  ; Torch7]
- SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation [
```
seg
```
  ,
```
keypoints
```
  ; Github]
- A Point Set Generation Network for 3D Object Reconstruction From a Single Image [
```
reconstruction
```
  ; Tensorflow]
- Multi-View 3D Object Detection Network for Autonomous Driving [
```
det
```
  ,
```
autonomous driving
```
  ; Tensorflow]
- Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image [
```
autonomous driving
```
  ]
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [
```
cls
```
  ,
```
seg
```
  ; Tensorflow]
- 3D Bounding Box Estimation Using Deep Learning and Geometry [
```
det
```
  ]
- OctNet: Learning Deep 3D Representations at High Resolutions [
```
cls
```
  ,
```
seg
```
  ,
```
orientation estimation
```
  ; PyTorch]
- 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions [
```
match
```
  ,
```
registration
```
  ; project]
- 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder [
```
registration
```
  ; github]
ICCV
- High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference [
```
completion
```
  ]
- Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models [
```
cls
```
  ,
```
retrieval
```
  ,
```
seg
```
  ; PyTorch-unofficial]
- Learning Compact Geometric Features [
```
registration
```
  ; Github]
- 2D-Driven 3D Object Detection in RGB-D Images [
```
det
```
  ]
Others
- Pointnet++: Deep hierarchical feature learning on point sets in a metric space [
```
cls
```
  ,
```
seg
```
  ; Tensorflow; NIPS]
- Deep Sets [PyTorch;
```
cls
```
  ]
- 3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection [
```
det
```
  ,
```
autonomous driving
```
  ; TPAMI]
- O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis [
```
cls
```
  ,
```
retrieval
```
  ,
```
seg
```
  ; Github; TOG]
- Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks [
```
det
```
  ; ICRA]
- 3d fully convolutional network for vehicle detection in point cloud [
```
det
```
  ; Tensorflow; IROS]
- Shape Completion Enabled Robotic Grasping [
```
completion
```
  ; Keras; IROS]
- SEGCloud: Semantic Segmentation of 3D Point Clouds [
```
seg
```
  ; 3DV]

Before 2016

2016
- Fast Global Registration [
```
registration
```
  ; ECCV; Github]
- Monocular 3D Object Detection for Autonomous Driving [CVPR]
- Volumetric and Multi-View CNNs for Object Classification on 3D Data [CVPR]
- Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients [CVPR]
- Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images [CVPR]
- Fpnn: Field probing neural networks for 3d data [NIPS]
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network [RSS]
2015
- Robust Reconstruction of Indoor Scenes [
```
reconstruction
```
  ; CVPR]
- Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration [
```
registration
```
  ; TPAMI; Github]
- 3D ShapeNets: A Deep Representation for Volumetric Shapes [CVPR]
- SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite [CVPR]
- Data-Driven 3D Voxel Patterns for Object Category Recognition [CVPR]
- Multi-view convolutional neural networks for 3d shape recognition [ICCV]
- 3d object proposals for accurate object class detection [NIPS]
- Voting for Voting in Online Point Cloud Object [RSS]
- Voxnet: A 3d convolutional neural network for real-time object recognition [IROS]
2014
- Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects [CVPR]
- Sliding shapes for 3d object detection in depth images [ECCV]
2012
- Are we ready for autonomous driving? the kitti vision benchmark suite [CVPR]
2009
- Fast point feature histograms (FPFH) for 3D registration [
```
registration
```
  ; ICRA]
- Generalized-ICP [
```
registration
```
  ; RSS]
1992
- A method for registration of 3-D shapes [
```
registration
```
  ; TPAMI]
1987
- Least-squares fitting of two 3-D point sets [
```
registration
```
  ; TPAMI]